ORION%20Project-team

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ORION Project-team

• Monique THONNAT
• INRIA Sophia Antipolis

Creation July 1995 Multidisciplinary team
artificial intelligence, software engineering,
computer vision
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Contents

• Team Presentation
• Research Directions
• Cognitive Vision 2002-2006
• Reusable Systems 2002-2006
• Objectives for the next Period

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Team presentation (May 2006)

• 4 Research Scientists François Bremond (CR1
  Inria)
• Sabine Moisan (CR1 Inria, HDR)
• Annie
  Ressouche (CR1 Inria)
• (team leader) Monique Thonnat (DR1 Inria)
• 1 External Collaborator Jean-Paul Rigault
  (Prof. UNSA Inria secondment)
• 4 Temporary Engineers Etienne Corvee, Ruihua
  Ma, Valery Valentin,
  Thinh Van Vu
• 7 PhD Students Bui Binh, Bernard
  Boulay, Naoufel Kayati,
• Le Thi Lan,
  Mohamed Becha Kaaniche,
• Vincent
  Martin, Marcos Zuniga

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Research directions

• Objective
• Intelligent Reusable Systems for Cognitive
  Vision
• Cognitive Vision
• Interpretation of static images
• Video understanding
• Reusable Systems
• Program Supervision
• LAMA Software platform

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Orion team positioning

• Cognitive Vision
• Image interpretation (ECVision European network
  on cognitive vision, EUCognition) vs. computer
  vision (INRIA CogB)
• Video understanding (USC Los Angeles, Georgia
  Tech. Atlanta, Univ. Central Florida, NUCK
  Taiwan, Univ. Kingston UK, INRIA Prima)
• Reusable Systems
• Program supervision e.g., scheduling (ASPEN and
  CASPER at JPL), image processing (Hermès at
  Univ. Caen, ExTI at IRIT)
• Platform approach e.g., ontology management
  (Protegé at Stanford), frameworks for multi
  agents (Aglets, Jade, Oasis at LIP6), distributed
  object community (Oasis at INRIA Sophia)

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Cognitive Vision Image Interpretation
2002-2006

• Objective semantic interpretation of static 2D
  images
• Recognition of object categories (versus
  individuals)
• Recognition of scenes involving several objects
  with spatial reasoning
• Intelligent management of image processing
  programs
• Towards a cognitive
  vision platform

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Cognitive Vision Image Interpretation
2002-2006

• Scientific achievements
• Knowledge acquisition
• A visual concept ontology with 144 spatial, color
  and texture concepts MVA04
• Learning
• Visual concept detectors IVC06
• Image segmentation parameters ICVSa06
• Cognitive vision platform
• Architecture ICVS03
• Object class recognition algorithm CIVR05

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Cognitive Vision Image Interpretation
2002-2006

• Self Assessment
• Strong points
• Visual concept ontology as user-friendly
  intermediate layer between image processing and
  application domain
• Automatic building of the visual concept
  detectors
• Still open issues
• Learning for image segmentation
• Temporal visual concept ontology

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Cognitive Vision Video Understanding 2002-2006

• Objective
• Real time recognition of interesting behaviors
• How?
• Data captured by video surveillance cameras
• Original video understanding approach mixing
• computer vision 4D analysis (3D temporal
  analysis)
• artificial intelligence a priori knowledge
  (scenario, environment)
• software engineering reusable VSIP platform

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Cognitive Vision Video Understanding
2002-2006
Objective Interpretation of videos from pixels
to alarms
Segmentation
Classification
Scenario Recognition
Tracking
Alarms
access to forbidden area
3D scene model Scenario models
A priori Knowledge
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Cognitive Vision Video Understanding
2002-2006

• Scientific achievements
• Multi-sensor video understanding
• 2 to 4 video cameras overlapping or not
  IDSS03,JASP05
• Video cameras optical cells contact sensors
  AVSS05
• Learning
• parameter tuningMVAa06
• frequent temporal scenarios models ICVSb06
• Temporal scenario
• a new real time recognition algorithm
  IJCAI03,ICVS03
• a new representation language MVAb06,ECAI02,KES02
  

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Cognitive Vision Video Understanding
2002-2006

• Industrial impact
• Strong impact in visual surveillance (metro
  station, bank agency, building access control,
  onboard train, airport)
• 4 European projects (ADVISOR, AVITRACK, SERKET,
  CARETAKER)
• 5 industrial contracts with RATP, ALSTOM, SNCF,
  Credit Agricole, STMicroelectronics
• 2 transfer activities with BULL (Paris), VIGITEC
  (Brussels)
• Creation of a start-up Keeneo July 2005 (8
  persons) for industrialization and exploitation
  of VSIP library.

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Cognitive Vision Video Understanding
2002-2006
Intelligent video surveillance of Bank agencies
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Cognitive Vision Video Understanding 2002-2006

• Unloading Global Operation

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Cognitive Vision Video Understanding
2002-2006

• Airport Apron Monitoring Unloading Operation
• European AVITRACK project

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Cognitive VisionVideo Understanding 2002-2006

• Self Assessment
• Strong points
• Video understanding approach real time,
  effective techniques used by external academic
  and industrial teams
• Launch of an evaluation competition for video
  surveillance algorithms (ETISEO) with currently
  25 international teams
• Still open issues
• Learning
• Multi sensor

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Reusable Systems Program Supervision

• Reusable Systems original approach for the
  reuse of programs with program supervision
  techniques
• Program supervision
• Automate the (re)configuration and execution of
  programs
• selection, scheduling, execution, and control of
  results
• Knowledge-based approach knowledge modeling,
  planning techniques, ..

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Reusable Systems LAMA Platform

• Reusable Systems
• Reuse of tools to design knowledge-based systems
  (KBS)
• LAMA Software Platform
• Set of toolkits to facilitate design and
  evolution of KBS elements
• engines, GUI, knowledge languages, learning and
  verification facilities
• Software Engineering approach genericity,
  frameworks, objects and components

LAMA
raise new issues, to be abstracted into new
components
provide generic components and tools
Problem Solving KBS
Virtuous Circle
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Reusable Systems LAMA Platform
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Reusable Systems Program Supervision 2002-2006

• Scientific achievements
• Improvement of the Pegase engine (Pegase)
• Multithreading, extensions to the YAKL language
  ECAI02
• Distributed program supervision
• Supervision Web server, multi-agent techniques,
  interoperability Pegase/Java/agents TC06
• Cooperation with image and video understanding
• Object recognition task using program supervision
  ICTAI03
• Interoperability with VSIP program supervision
  for video understanding ICVSc06

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Reusable Systems LAMA Platform 2002-2006

• Scientific achievements
• Enforcing LAMA safe usage
• Verification of LAMA component extensions relying
  on Model Checking approach Informatica01,
  SEFM04
• Encompassing new tasks
• Classification and object recognition in images
  new engine and new knowledge representation
  language ICTAI03
• Model calibration in hydraulics new
  engine/language (PhD co-directed with INPT and
  CEMAGREF) KES03, JH05

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Reusable Systems Self Assessment

• Strong points
• Real time performance (Pegase and video)
• Using program supervision costs less than 5 of
  overall processing time
• LAMA genericity at work
• Different tasks (supervision, classification,
  calibration) in various application domains
  (hydraulics, biology, astronomy, video
  surveillance)
• Shorter development time and safer code
• Reuse of concepts as well as code
• Several variants of a task sharing common
  concepts
• Extensibility and commitment to Standards

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Objectives for the next period 1/5

• Creation of a new INRIA project-team PULSAR
• Perception Understanding and Learning Systems
  for Activity Recognition
• Theme
• CogC Multimedia data interpretation and
  man-machine interaction
• Multidisciplinary team
• artificial intelligence, software engineering,
  computer vision
• Objective
• Research on Cognitive Systems for Activity
  Recognition
• Focus on spatiotemporal activities of physical
  objects
• From sensor output to high level interpretation

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Objectives for the next period 2/5

• PULSAR Scientific objectives
• Two research axes
• Scene Understanding for Activity Recognition
• Generic Components for Activity Recognition
• PULSAR Applications
• Safety/security (e.g. intelligent surveillance)
• Healthcare (e.g. assistance to the elderly)

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Objectives for the next period 3/5

• PULSAR Scene Understanding for Activity
  Recognition
• Perception multi-sensors, finer descriptors
• Understanding uncertainty, 4D coherency,
  ontology for AR
• Learning parameter setting, event detector,
  activity models, program supervision KB (risky
  objective)

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Objectives for the next period 4/5

• PULSAR Generic Components for Activity
  Recognition
• From LAMA Platform to AR platform
• Model extensions
• modeling time and scenarios
• handling uncertainty
• User-friendliness and safeness of use
• theory and tools for component frameworks
• scalability of verification methods
• Architecture improvement
• parallelization, distribution, concurrence
• real time response
• domain specific software and graphical interface
  plugging

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Objectives for the next period 5/5

• Short term objectives
• Scene Understanding for Activity Recognition
• Perception gesture analysis
• Understanding
• ontology-based activity recognition
• uncertainty management
• Learning primitive event detectors learning
• Generic Components for Activity Recognition
• Model of time and scenarios
• Internal concurrency and distributed architecture